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Exploring Spatial Patterns of Interurban Passenger Flows Using Dual Gravity Models
Geographical gravity models can be employed to quantitatively describe and predict spatial flows, including migration flows, passenger flows, daily commuting flows, etc. However, how to model spatial flows and reveal the structure of urban traffic networks in the case of missing partial data is stil...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778205/ https://www.ncbi.nlm.nih.gov/pubmed/36554197 http://dx.doi.org/10.3390/e24121792 |
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author | Wang, Zihan Chen, Yanguang |
author_facet | Wang, Zihan Chen, Yanguang |
author_sort | Wang, Zihan |
collection | PubMed |
description | Geographical gravity models can be employed to quantitatively describe and predict spatial flows, including migration flows, passenger flows, daily commuting flows, etc. However, how to model spatial flows and reveal the structure of urban traffic networks in the case of missing partial data is still a problem to be solved. This paper is devoted to characterizing the interurban passenger flows in the Beijing–Tianjin–Hebei region of China using dual gravity models and Tencent location big data. The method of parameter estimation is the least squares regression. The main results are as follows. First, both the railway and highway passenger flows can be effectively described by dual gravity models. A small part of missing spatial data can be compensated for by predicted values. Second, the fractal properties of traffic flows can be revealed. The railway passenger flows follow the gravity scaling law better than the highway passenger flows. Third, the prediction residuals indicate the changing trend of interurban connections in the study area in recent years. The center of gravity of the spatial dynamics has shifted from the Beijing–Tianjin–Tangshan triangle to the Beijing–Baoding–Shijiazhuang axis. A conclusion can be reached that the dual gravity model is an effective tool for analyzing spatial structures and dynamics of traffic networks and flows. Moreover, the model provides a new approach to estimating the fractal dimensions of traffic networks and spatial flow patterns. |
format | Online Article Text |
id | pubmed-9778205 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97782052022-12-23 Exploring Spatial Patterns of Interurban Passenger Flows Using Dual Gravity Models Wang, Zihan Chen, Yanguang Entropy (Basel) Article Geographical gravity models can be employed to quantitatively describe and predict spatial flows, including migration flows, passenger flows, daily commuting flows, etc. However, how to model spatial flows and reveal the structure of urban traffic networks in the case of missing partial data is still a problem to be solved. This paper is devoted to characterizing the interurban passenger flows in the Beijing–Tianjin–Hebei region of China using dual gravity models and Tencent location big data. The method of parameter estimation is the least squares regression. The main results are as follows. First, both the railway and highway passenger flows can be effectively described by dual gravity models. A small part of missing spatial data can be compensated for by predicted values. Second, the fractal properties of traffic flows can be revealed. The railway passenger flows follow the gravity scaling law better than the highway passenger flows. Third, the prediction residuals indicate the changing trend of interurban connections in the study area in recent years. The center of gravity of the spatial dynamics has shifted from the Beijing–Tianjin–Tangshan triangle to the Beijing–Baoding–Shijiazhuang axis. A conclusion can be reached that the dual gravity model is an effective tool for analyzing spatial structures and dynamics of traffic networks and flows. Moreover, the model provides a new approach to estimating the fractal dimensions of traffic networks and spatial flow patterns. MDPI 2022-12-08 /pmc/articles/PMC9778205/ /pubmed/36554197 http://dx.doi.org/10.3390/e24121792 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wang, Zihan Chen, Yanguang Exploring Spatial Patterns of Interurban Passenger Flows Using Dual Gravity Models |
title | Exploring Spatial Patterns of Interurban Passenger Flows Using Dual Gravity Models |
title_full | Exploring Spatial Patterns of Interurban Passenger Flows Using Dual Gravity Models |
title_fullStr | Exploring Spatial Patterns of Interurban Passenger Flows Using Dual Gravity Models |
title_full_unstemmed | Exploring Spatial Patterns of Interurban Passenger Flows Using Dual Gravity Models |
title_short | Exploring Spatial Patterns of Interurban Passenger Flows Using Dual Gravity Models |
title_sort | exploring spatial patterns of interurban passenger flows using dual gravity models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778205/ https://www.ncbi.nlm.nih.gov/pubmed/36554197 http://dx.doi.org/10.3390/e24121792 |
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